公开(公告)号：US20240004938A1

公开(公告)日：2024-01-04

申请号：US18217072

申请日：2023-06-30

申请人： Giant Oak, Inc.

发明人： Gary Shiffman ,  Jeffrey Borowitz

IPC分类号： G06F16/951 ,  G06F16/93 ,  G06F16/33 ,  G06F16/338

CPC分类号： G06F16/951 ,  G06F16/93 ,  G06F16/334 ,  G06F16/338 ,  G06F16/3344

摘要： In some implementations, systems and methods that are capable of customizing negative media searches using domain-specific search indexes are described. Data indicating a search query associated with a negative media search for an entity and a corpus of documents to be searched are obtained. Content from a particular collection of documents from among the corpus of documents is obtained and processed. Multiple scores for the entity are computed based on processing the content obtained from the collection of documents. The multiple scores are aggregated to compute a priority indicator that represents a likelihood that the collection of documents includes content that is descriptive of derogatory information.

公开(公告)号：US11693907B2

公开(公告)日：2023-07-04

申请号：US17140613

申请日：2021-01-04

申请人： Giant Oak, Inc.

发明人： Gary Shiffman ,  Jeffrey Borowitz

IPC分类号： G06F16/00 ,  G06F16/951 ,  G06F16/93 ,  G06F16/33 ,  G06F16/338

CPC分类号： G06F16/951 ,  G06F16/334 ,  G06F16/338 ,  G06F16/3344 ,  G06F16/93

摘要： In some implementations, systems and methods that are capable of customizing negative media searches using domain-specific search indexes are described. Data indicating a search query associated with a negative media search for an entity and a corpus of documents to be searched are obtained. Content from a particular collection of documents from among the corpus of documents is obtained and processed. Multiple scores for the entity are computed based on processing the content obtained from the collection of documents. The multiple scores are aggregated to compute a priority indicator that represents a likelihood that the collection of documents includes content that is descriptive of derogatory information.

公开(公告)号：US20200184487A1

公开(公告)日：2020-06-11

申请号：US16210942

申请日：2018-12-05

申请人： Giant Oak, Inc.

发明人： Harsh Pandya ,  Jacob Shapiro ,  Gary Shiffman

IPC分类号： G06Q30/00 ,  G06N20/00

摘要： Systems and techniques are described for applying machine learning techniques to dynamically identify potentially anomalous activity of entities. In some implementations, peer group data is obtained. The peer group data indicates multiple entities classified as belonging to a particular peer group, and a set of attributes associated with the multiple entities. Transaction data for the multiple entities is obtained from one or more data sources. One or more transaction models are selected. The transaction models that are each trained to apply a particular set of evidence factors corresponding to the set of attributes associated with the multiple entities, and identify transaction patterns representing potentially anomalous activity. The transaction data is processed using the one or more transaction models to identify potentially anomalous activity within the transaction data for the multiple entities. A prioritization indicator is computed for each entity included in the multiple entities.

公开(公告)号：US11836739B2

公开(公告)日：2023-12-05

申请号：US16210942

申请日：2018-12-05

申请人： Giant Oak, Inc.

发明人： Harsh Pandya ,  Jacob Shapiro ,  Gary Shiffman

IPC分类号： G06Q30/018 ,  G06N20/00 ,  H04L9/40

CPC分类号： G06Q30/0185 ,  G06N20/00 ,  H04L63/30

摘要： Systems and techniques are described for applying machine learning techniques to dynamically identify potentially anomalous activity of entities. In some implementations, peer group data is obtained. The peer group data indicates multiple entities classified as belonging to a particular peer group, and a set of attributes associated with the multiple entities. Transaction data for the multiple entities is obtained from one or more data sources. One or more transaction models are selected. The transaction models that are each trained to apply a particular set of evidence factors corresponding to the set of attributes associated with the multiple entities, and identify transaction patterns representing potentially anomalous activity. The transaction data is processed using the one or more transaction models to identify potentially anomalous activity within the transaction data for the multiple entities. A prioritization indicator is computed for each entity included in the multiple entities.

公开(公告)号：US20220005042A1

公开(公告)日：2022-01-06

申请号：US17365807

申请日：2021-07-01

申请人： Giant Oak, Inc.

发明人： Gary Shiffman ,  Jacob Shapiro

IPC分类号： G06Q20/40 ,  G06Q20/38 ,  G06K9/62

摘要： Systems and techniques are described for orchestrating iterative updates to machine learning models (e.g., transaction models) deployed to multiple end-user devices. In some implementations, output data generated by a first transaction model deployed at a first end-user device is obtained. The first transaction model is trained to apply a set of evidence factors to identify potentially anomalous activity associated with a first target entity. An adjustment for a second transaction model deployed at a second end-user device is determined. The second transaction model is trained to apply the set of evidence factors to identify potentially anomalous activity associated with a second target entity determined to be similar to the first target entity. A model update for the second transaction model is generated. The model update specifies a change to the second transaction model. The model update is provided for output to the second end-user device.

公开(公告)号：US20210157863A1

公开(公告)日：2021-05-27

申请号：US17140613

申请日：2021-01-04

申请人： Giant Oak, Inc.

发明人： Gary Shiffman ,  Jeffrey Borowitz

IPC分类号： G06F16/951 ,  G06F16/93 ,  G06F16/33 ,  G06F16/338

摘要： In some implementations, systems and methods that are capable of customizing negative media searches using domain-specific search indexes are described. Data indicating a search query associated with a negative media search for an entity and a corpus of documents to be searched are obtained. Content from a particular collection of documents from among the corpus of documents is obtained and processed. Multiple scores for the entity are computed based on processing the content obtained from the collection of documents. The multiple scores are aggregated to compute a priority indicator that represents a likelihood that the collection of documents includes content that is descriptive of derogatory information.

公开(公告)号：US10885124B2

公开(公告)日：2021-01-05

申请号：US15451069

申请日：2017-03-06

申请人： Giant Oak, Inc.

发明人： Gary Shiffman ,  Jeffrey Borowitz

IPC分类号： G06F16/00 ,  G06F16/951 ,  G06F16/93 ,  G06F16/33 ,  G06F16/338

摘要： In some implementations, systems and methods that are capable of customizing negative media searches using domain-specific search indexes are described. Data indicating a search query associated with a negative media search for an entity and a corpus of documents to be searched are obtained. Content from a particular collection of documents from among the corpus of documents is obtained and processed. Multiple scores for the entity are computed based on processing the content obtained from the collection of documents. The multiple scores are aggregated to compute a priority indicator that represents a likelihood that the collection of documents includes content that is descriptive of derogatory information.

公开(公告)号：US20170255700A1

公开(公告)日：2017-09-07

申请号：US15451069

申请日：2017-03-06

申请人： Giant Oak, Inc.

发明人： Gary Shiffman ,  Jeffrey Borowitz

IPC分类号： G06F17/30

CPC分类号： G06F16/951 ,  G06F16/334 ,  G06F16/3344 ,  G06F16/338 ,  G06F16/93

摘要： In some implementations, systems and methods that are capable of customizing negative media searches using domain-specific search indexes are described. Data indicating a search query associated with a negative media search for an entity and a corpus of documents to be searched are obtained. Content from a particular collection of documents from among the corpus of documents is obtained and processed. Multiple scores for the entity are computed based on processing the content obtained from the collection of documents. The multiple scores are aggregated to compute a priority indicator that represents a likelihood that the collection of documents includes content that is descriptive of derogatory information.